Medical diagnostic image change highlighter

ABSTRACT

Systems and methods are disclosed which enable more accurate examination of medical diagnostic images, for example x-ray, ultrasound and magnetic resonance imaging (MRI) images. The systems and methods highlight anomalies that have changed between the collection times of two or more diagnostic images, and can also provide objective scoring of the degree of change.

TECHNICAL FIELD

The invention relates generally to image rendering. More particularly,and not by way of any limitation, the present application relates togenerating an image that highlights differences between medicaldiagnostic images.

BACKGROUND

When a dentist is attempting to determine whether an apparent anomaly ina patient's recent dental x-ray image merits further investigation andtreatment, the dentist will often compare the recent x-ray image withone taken at a prior time. This is typically accomplished by placingboth x-ray images within the dentist's field of view, perhaps on asingle computer monitor, but as separate images. The dentist thenalternates focus between the two images, in order to ascertain whetherthe apparent anomaly is new, has worsened over time, or else hasremained fairly unchanged. If the apparent anomaly is new, or hasworsened over time, the dentist may suspect the recent formation of acavity or other damage to the patient's teeth.

Other medical professionals may perform a similar procedure usingultrasound images, magnetic resonance imaging (MRI) images, or othermedical diagnostic images, to diagnose other medical conditions. Theprofessionals use their own judgment, which can vary according toexperience and other factors, to determine whether the amount of changeis problematic, based on the time difference between when the differentimages were collected. Thus, current change analysis is subjective, andcan potentially be inconsistent.

Unfortunately, there are multiple shortcomings with the above procedure:There is a possibility that a new anomaly in a diagnostic image may bemissed by the medical professional, and also there is no objective scoreto quantify differences between the images. These problems can result inaccusations of sub-standard care by medical malpractice attorneys if apatient later claims that a developing medical problem was notidentified in the images.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention, reference isnow made to the following descriptions taken in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates color mixing.

FIG. 2 illustrates a 3-dimensional color cube.

FIG. 3 illustrates a block diagram for generating a medical diagnosticcomparison image.

FIG. 4 illustrates a set of baseline medical diagnostic images and acomparison image on a display.

FIG. 5 illustrates a rotation adjustment of one baseline medicaldiagnostic image relative to another baseline medical diagnostic image.

FIG. 6 illustrates a magnification adjustment of one baseline medicaldiagnostic image relative to another baseline medical diagnostic image.

FIG. 7 illustrates a horizontal displacement adjustment of one baselinemedical diagnostic image relative to another baseline medical diagnosticimage.

FIG. 8 illustrates a vertical displacement adjustment of one baselinemedical diagnostic image relative to another baseline medical diagnosticimage.

FIG. 9 illustrates an intensity adjustment of one baseline medicaldiagnostic image relative to another baseline medical diagnostic image.

FIG. 10 illustrates another block diagram for generating a medicaldiagnostic comparison image.

FIG. 11 illustrates a plot of pixel intensity difference values along arow or column of a pixel intensity matrix.

FIG. 12 illustrates scoring criteria for a medical diagnostic comparisonimage.

FIG. 13 illustrates another block diagram for generating a medicaldiagnostic comparison image.

FIG. 14 illustrates a medical diagnostic comparison image generatingsystem.

FIG. 15 illustrates a method of generating a medical diagnosticcomparison image.

DETAILED DESCRIPTION OF THE INVENTION

Systems and methods are disclosed which enable more accurate examinationof medical diagnostic images, for example x-ray, ultrasound and magneticresonance imaging (MRI) images. This is accomplished by generating acomparison image that highlights changes for medical professionals, suchas doctors and dentists, between two medical diagnostic images that werecollected at different times. Embodiments of the disclosed systems andmethods highlight anomalies that have changed between the collectiontimes of two or more diagnostic images, and can also optionally provideobjective scoring of the degree of change.

FIG. 1 illustrates a color mixing diagram 100, explaining how whitelight can be created by combining various different colors. For example,a combination of red, green and blue can create white, if the red, greenand blue components are properly balanced. Combinations of two of thethree colors can create other colors. As illustrated, green and blue arecombined to create cyan.

FIG. 2 illustrates a 3-dimensional color cube 200, which also representscolor mixing options. Red is illustrated as an axis of the cube, as aregreen and blue. Any specific color can be achieved simply by mixing aselected intensity of the red, green and blue color components. For cube200, the intensity of a particular color component is represented as adistance away from black, along one of the color component axes. Toexplain the color cube, the black corner can be addressed first. Theabsence of any color, which occurs when all of red, green and blue areset to zero intensity, is black. Mixing a full intensity of red andgreen, but with no blue, creates yellow. Adding a full intensity of blueto yellow creates white. Mixing a full intensity of green and blue, butwith no red, creates cyan. Adding a full intensity of red to cyancreates white. Mixing a full intensity of red and blue, but no green,creates magenta. Adding a full intensity of green to magenta createswhite.

For 24-bit color bitmaps, which are common in computer graphics, colorintensity is often scaled between 0 and 255, with 255 representing fullintensity. Therefore, with a 24-bit color bitmap image, a pixel having a255 level of each of red, green, and blue is a white pixel. A pixelhaving equal red, green and blue levels below 255 is gray. Therefore,the color gray can be considered to be a color axis running diagonalfrom the black corner of color cube 200, in a straight line to the mostdistant corner of the cube, which is the white corner.

FIG. 3 illustrates a block diagram 300 for generating a medicaldiagnostic comparison image 301. A baseline medical diagnostic image301, which is received into a computer readable medium, is processedaccording to a processing method 302, to create a baseline pixel matrix303, wherein baseline pixel matrix 303 represents an intensity of pixelsin at least a portion of baseline medical diagnostic image 301. Abaseline medical diagnostic image 304, which is received into a computerreadable medium, is processed according to a processing method 305, tocreate a baseline pixel matrix 306, wherein baseline pixel matrix 306represents an intensity of pixels in at least a portion of baselinemedical diagnostic image 304. Processing methods 302 and 305 may includeadjusting any of rotation, magnification, horizontal displacement,vertical displacement, and intensity.

Creating a comparison image 307 can be accomplished by using baselinepixel matrix 303 to provide red pixel intensities and baseline pixelmatrix 306 to provide cyan pixel intensities. To the extent thatcorresponding pixels in matrices 303 and 306 are equal, comparison image307 will be grayscale. There may be some differences among the pixelintensity values, but if the differences are a relatively minorpercentage of the intensity values, comparison image 307 will bereasonably close to gray.

However, as illustrated, there is a bright region 308, within baselinepixel matrix 303, in which pixel intensities exceed the intensity ofcorresponding pixels in the baseline pixel matrix 306. Because the pixelintensities are imbalanced, the corresponding pixels in comparison image307 will have a colored hue. Since baseline pixel matrix 303 providesthe red color information, the hue will be red. This is indicated asred-hued region 309, within comparison image 307. Similarly, there is abright region 310, within baseline pixel matrix 306, in which pixelintensities exceed the intensity of corresponding pixels in the baselinepixel matrix 303. Because the pixel intensities are imbalanced, thecorresponding pixels in comparison image 307 will have a colored hue.Since baseline pixel matrix 306 provides the cyan color information, thehue will be cyan. This is indicated as cyan-hued region 311, withincomparison image 307.

For the case in which two identical baseline images are used in theprocess, the output will be a purely grayscale image. However, if thepixel intensities for most of the corresponding pixels in each ofmatrices 303 and 306 are close enough that comparison image 307 appearsgray to a human observer, with some regions of red or cyan hue, as notedabove, comparison image 307 will only be a predominantly grayscaleimage.

FIG. 4 illustrates a display 400, having a video display screen 401,which is showing a comparison image 402, a baseline medical diagnosticimage 403 and another baseline medical diagnostic image 404. A medicalprofessional may wish to see not only comparison image 402, but alsobaseline medical diagnostic images 403 and 404, simultaneously withcomparison image 402, in order to diagnose changed medical conditionsfor a patient. In one example use, baseline medical diagnostic image 403is the currently-collected image, perhaps collected just minutes orseconds prior to the creation of comparison image 402, and baselinemedical diagnostic image 404 is an older image, perhaps collected duringa patent's prior visit to the medical professional. In some uses,baseline medical diagnostic image 404 could have been in the patient'smedical history, collected by a different medical professional andacquired over a computer network. Either originally-collected imagescould be used, processed images from any stage of the registrationprocess, zoomed-in portions, or any combination. Although three imagesare illustrated, it should be understood that a different number ofimages could be used.

As illustrated, comparison image 402 highlights a region 405 of toothwear, which can be identified using dental x-ray images. Comparisonimage 402 also highlights a region 406 that indicates a cavity in one ofthe patient's teeth. Region 407, which is a region of abnormalintensity, corresponds to a dental filling, and should be fairly closeto gray. However, regions of abnormal brightness or darkness in baselineimages may be subject to tinting in the comparison image, due todifferences in the collections of the images at different times. Thesedifferences may include the use of different equipment or differentimaging angles. One reason that the medical professional may wish to seethe images simultaneously is to be able to ascertain that region 406 hasa corresponding abnormal region 408 within diagnostic image 403, but notdiagnostic image 404, and that region 407 has corresponding abnormalregions 409 within both diagnostic image 403 and diagnostic image 404.

From a quick scan of comparison image 402 and baseline medicaldiagnostic images 403 and 404 on screen 401, a dentist can quicklyascertain tooth wear, the formation of a new cavity, and identify afilling as predating the earlier image 404.

In order to form a useful comparison image, though, two or threebaseline images should be as close to identical as practical, so thatthe largest and most brightly hued regions correspond to meaningfuldifferences, such as changed medical conditions, rather than differencesin image collections. Since it is possible that the baseline medicaldiagnostic images were collected differently, adjustments may be neededfor rotation, magnification, horizontal displacement, verticaldisplacement, and intensity—both average and extremes. Such adjustmentsare known in the art, and may use averaging, edge detection, andinterpolation. In many cases, the individual steps of minimizingdifferences between two images may be iterative. For example, smalladjustments can be made in rotation, then magnification, and thenrotation may be adjusted again. In some embodiments, such adjustmentscan be accomplished under human control, with a comparison image madeafter each adjustment, and with the human attempting to minimize thehued regions in the comparison image. Since an objective scoring methodis described later, the image alignment process can be automated, withthe controlling algorithm iterating adjustments and scoring in anattempt to minimize the objective difference score.

FIG. 5 illustrates a rotation adjustment of baseline medical diagnosticimage 501 relative to baseline medical diagnostic image 404, to produceadjusted baseline medical diagnostic image 502 in process 500. In someembodiments, adjusting a rotation of a baseline pixel matrix comprisescalculating a pixel matrix using one of a nearest neighbor method, alinear interpolation method, and a polynomial interpolation method; andreplacing the initial baseline pixel matrix with the new pixel matrix.This new pixel matrix forms the pixel intensity information for adjustedbaseline medical diagnostic image 502. Rotation of one image relative toanother, in order to automatically align the images, is known in the artand is commonly performed in computer graphics functions. In someembodiments, a human could control the rotation process. Althoughrotation of only one image is illustrated, it should be understood thateither or both images could be rotated.

FIG. 6 illustrates a magnification adjustment of baseline medicaldiagnostic image 601 relative to baseline medical diagnostic image 404,to produce adjusted baseline medical diagnostic image 602 in process600. In some embodiments, adjusting a magnification of a baseline pixelmatrix comprises calculating a pixel matrix using one of a nearestneighbor method, a linear interpolation method, and a polynomialinterpolation method; and replacing the initial baseline pixel matrixwith the new pixel matrix. This new pixel matrix forms the pixelintensity information for adjusted baseline medical diagnostic image602. Adjustment of image magnification of one image relative to another,in order to automatically align the images, is known in the art and iscommonly performed in computer graphics functions. In some embodiments,a human could control the magnification adjustment process. Althoughmagnification adjustment of only one image is illustrated, it should beunderstood that either or both images could be adjusted formagnification.

FIG. 7 illustrates a horizontal displacement adjustment of baselinemedical diagnostic image 701 relative to baseline medical diagnosticimage 404, to produce adjusted baseline medical diagnostic image 702 inprocess 700. In some embodiments, adjusting a displacement of a baselinepixel matrix comprises generating a new pixel matrix based on a croppedversion of the baseline pixel matrix; and replacing the baseline pixelmatrix with the new pixel matrix. This new pixel matrix forms the pixelintensity information for adjusted baseline medical diagnostic image702. In some embodiments, both images will require cropping.Displacement adjustment of one image relative to another, in order toautomatically align features within the images, is known in the art andis commonly performed in computer graphics functions. In someembodiments, a human could control the translation and cropping process.Although adjustment of only one image is illustrated, it should beunderstood that either or both images could be adjusted.

FIG. 8 illustrates a vertical displacement adjustment of baselinemedical diagnostic image 801 relative to baseline medical diagnosticimage404, to produce adjusted baseline medical diagnostic image 802 inprocess 800. Although adjustment of only one image is illustrated, itshould be understood that either or both images could be adjusted.

FIG. 9 illustrates an intensity adjustment of baseline medicaldiagnostic image 900 relative to baseline medical diagnostic image 404,to produce adjusted baseline medical diagnostic image 902 in process700. In some embodiments, adjusting pixel intensity comprises adjustingaverage intensity, minimum intensity, maximum intensity, contrast, andvarious combinations. Adjustments may be linear or non-linear. It shouldbe understood that the afore-mentioned processes could be performed onimage pixels directly, while they reside within computer memoryformatted as image color information, or else the pixel intensitiescould be copied into normal matrices, operated upon, and then thesematrices could be used to create new images or replace the pixel valueswithin existing images. In some embodiments, a human could control theintensity adjustment process. Although adjustment of only one image isillustrated, it should be understood that either or both images could beadjusted. Together, FIGS. 5 through 9 illustrate an exemplary imageregistration process.

FIG. 10 illustrates another block diagram 1000 for generating a medicaldiagnostic comparison image 1007. Baseline images 1001, 1002 and 1003are used to create red matrix 1004, green matrix 1005 and blue matrix1006, respectively. The formation of an image in this manner creates athree-color multi-view, rather than a two-color multi-view (2CMV), whichwas illustrated in FIG. 3. It should be noted that some medicalprofessionals may prefer that the pixel intensities of the constituentcolor matrices are not enhanced in regions of pixel intensitydifferences among the multiple images. However, some medicalprofessionals may prefer that pixel intensity differences in the huedregions, in which the pixel intensities of the color components differ,be exaggerated, to more clearly highlight the color differences. Onemethod of doing this is to have a non-linear mapping of pixelintensities, such that if R−G=X for a pixel (R is the red intensity, Gis the green intensity), then for that pixel R is replaced with R+X/2and G with G−X/2. This would make a reddish pixel more deeply red, or agreenish pixel more brightly green.

Other color enhancement or difference exaggeration transforms could beused, such as multiplicative transforms. Color difference exaggerationcan also be used between red and cyan colors for two-color systems.Exaggerations of differences could be adjustable, such as by a userinputting a preference to vary color enhancement though a graphical userinterface (GUI). This can permit a medical services provider to tailorcolor enhancement to a preference, although such a visual displaypreference should not affect any objective difference scoring. That is,objective scoring could be accomplished with a consistent differencecalculation scheme.

In block diagram 1000, the generation process for comparison image 1007includes receiving a third baseline medical diagnostic image into acomputer readable medium; creating a third baseline pixel matrix,wherein the third baseline pixel matrix represents an intensity ofpixels in at least a portion of the third baseline medical diagnosticimage; adjusting a rotation of the third baseline pixel matricesrelative to the other baseline pixel matrices; adjusting a magnificationof the third baseline pixel matrices relative to the other baselinepixel matrices; adjusting a displacement of the third baseline pixelmatrices relative to the other baseline pixel matrices; and adjusting anintensity of at least a portion of the third baseline pixel matricesrelative to corresponding portions of the other baseline pixel matrices.Creating comparison image 1007 comprises creating a predominantlyintensity-only image with the third baseline pixel matrix providing athird color information. If about 80% or more of the pixels have thediffering colors intensities within approximately 10% of each other, thecomparison image will be predominantly grayscale. In some embodiments, adifferent intensity difference could be used, including either absolutedifferences or another percentage difference.

FIG. 11 illustrates a plot 1100 of pixel intensity difference valuesalong a row or column of a pixel intensity matrix. Plotted line 1101could be an absolute value or a signed value, based on whether a singlethreshold is used for scoring or whether positive and negativethresholds are used. Plotted line 1101 is the value of the pixelintensity difference between corresponding pixels in different colormatrices, for example matrices 303 and 306 of FIG. 3, as a function ofpixel position. The horizontal axis, “Pixel Position”, represents amatrix index number, and could be either a row or a column index. Thevertical axis, “Pixel Intensity Difference” is the value of thedifference. A threshold 1102 is illustrated, which could be either anabsolute number, or could represent a percentage difference, for example10% of the maximum pixel intensity in either of the images.

Plotted line 1101 exceeds threshold 1102 in two places. One is anomalouspoint 1103, which is only a single pixel. Anomalous point 1103 could bedue to measurement error or electrical noise within the imaging system.Anomalous point 1103 could be removed from consideration, and eliminatedas a distraction to a medical professional by using a moving averagewindow over plotted line 1101. Anomaly suppression in images iswell-known in the art, and may be added to many of the process describedherein.

Difference region 1104 is an area in which plotted line 1101 exceedsthreshold 1102 over an extended length. If pixels within differenceregion 1104 were also within a similar, extended difference region inthe orthogonal “Pixel Position” direction, then such pixels would bewithin a 2-dimensional difference region. The remaining smaller peaksand valleys in plotted line 1101 represent image noise.

FIG. 12 illustrates scoring criteria for a medical diagnostic comparisonimage 1200. Comparison image 1200 comprises three regions, 1201, 1202and 1203. The height, H, and width, W, of region 1203 are indicated,although a difference region could be any geometric shape, includingboth convex and concave shapes. Comparison image 1200 also includesanomalous pixel 1204. Relating FIG. 12 to FIG. 11, plotted line 1101represents a column of pixel intensity difference values that extendfrom the top to the bottom of comparison image 1200, through anomalouspixel 1204 and region 1202. Anomalous point 1103 corresponds toanomalous pixel 1204, and difference region 1104 extends verticallyacross region 1202, in this exemplary relation of the hypothetical datasets illustrated using FIGS. 11 and 12.

A method of scoring a comparison image may include comparing a region ofpixel intensity difference to both an average intensity differencethreshold and also a minimum dimension threshold. The dimensionthreshold could include multiple criteria, such as minimum span inorthogonal directions, as well as minimum area. Responsive to the regionof pixel intensity difference meeting or exceeding the average intensitydifference threshold and the minimum dimension threshold, the systemcould cause an alert to draw a medical professional's attention to theextent of the differences within a comparison image. However, such analert should be delayed until the comparison image formation process hasproduced the best alignment of the baseline images, in order to avoidcausing false alarms if the difference regions are due predominantly tomisalignment of the baseline images. Other alert criteria can also beused, such as a dimension of a difference region that meets or exceedsan average intensity difference threshold; a count of difference regionsthat meet or exceed an average intensity difference threshold and aminimum dimension threshold; and a time difference associated with theimages. For example, if the baseline images had been created yearsapart, more differences could be expected than if the images had beencreated only a few months apart.

Scoring of differences can also be performed without the rendering of acolor image, such as using the baseline images as input matrices to ascoring process without assigning color significance to either matrix. Adifference score can be calculated for a portion of an image such as aregion of interest, either selected manually by a user, or automaticallyby performing a segmentation process on the image. The same region ofinterest or a different region of interest may be included in thedisplayed image or images. One advantage of scoring only a subset of theimage views is that noise and spurious results in background areas canbe excluded. Examples of regions of interest can include a specifictooth, a set of teeth, a specific bone or set of bones, specific organs,and subsections of these examples. There is no need for a scored sectionto be rectangular, but instead could be defined by any closed curve,whether purely convex or having concavities. One possible scoringalgorithm is Score=(1/N)*SUM((T((I_(1RC)-I_(2RC)),t))^(E)), where N isthe number of pixels included in the scoring region used in the SUMsummation, T is a threshold function, I_(1RC) is the processed pixelintensity value of image 1 at row position R and column position C,I_(2RC) is the processed pixel intensity value of image 2, t is athreshold value, and E is an exponential factor. The (1/N) normalizesthe score, and T(A,t) returns 0 if A<t and A otherwise. For E>1, thescore will be weighted most heavily by large differences, even if onlyover a relatively small number of pixels. For E<1, the effect of a fewlarge differences will be muted in the entire score. It should beunderstood that it is merely an exemplary scoring algorithm, and thatother scoring algorithms may be used.

FIG. 13 illustrates another block diagram 1300 for generating a medicaldiagnostic comparison image 1309, which may be displayed for a medicalprofessional simultaneously with at least a portion of baseline medicaldiagnostic image 1301, at least a portion of baseline medical diagnosticimage 1302, or both. Baseline medical diagnostic image 1301 is processedaccording to the logic contained in process module 1302, and baselinemedical diagnostic image 1303 is processed according to the logiccontained in process module 1304. The processed results, which includerotation, magnification, displacement, and intensity adjustments, aresent to pixel comparison and adjustment control module 1305. Module 1305passes the results to anomaly suppression module 1306, which is thenused to create red matrix 1307 and cyan matrix 1308. Red matrix 1307 andcyan matrix 1308 are combined to create comparison image 1309.

A scoring module 1310 is illustrated as coupled to both pixel comparisonand adjustment control module 1305 and the output of anomaly suppressionmodule 1306. Scoring module 1310 can calculate objective scores based onthe pixel differences. The score could be a single number or else aweighted composite score that included the total area of all differenceregions and a total count of difference regions exceeding some minimumdimensions. Scoring module 1310 can be used for both feedback to enableautomated fine-tuning of the baseline image adjustments in processmodules 1302 and 1304, as well as for final scoring and generatingalerts. Final scoring, causing alerts for high scores, and pixeldifference exaggeration to more brightly highlight any differently-huedregions, should generally occur after the best possible fine-tuning ofthe baseline image alignment has been accomplished.

For an automated image alignment process, after receiving the baselineimages, a trial adjustment can be accomplished, perhaps by using an edgedetection process and feature extraction. Fine-tuning can be achieved byattempting to minimize a difference score, which could be a compositescore that included the total area of all difference regions and a totalcount of difference regions exceeding some minimum dimensions or area.The score minimization could be a trial and error process, could usegenetic algorithms, or could be predictive, using sensitivity analysis,in order to predict the optimum adjustments by comparing the changeafter multiple attempts. For example, an initial score is known for theinitial set of image adjustment parameters, including displacement,intensity, rotation, magnification, and perhaps another parameter. Aparticular parameter, identified as parameter P, is selected for trialadjustment. It is changed, and a new score is found. Based on the set ofknown scores, a new value of P is selected that should reduce the score.One way this can be accomplished is by treating the set of scores as afunction that is dependent upon P. This new value of P is tried, and theprocess repeats until the score cannot be lowered merely by changing P.Then another parameter, Q, is chosen for alteration. When the score isagain lowered to a minimum level for a particular P and Q, the nextparameter is chosen for alteration. When all parameters have beenindividually adjusted, the process starts with P again, until no morereduction is possible. Sensitivity analysis is known in the art forminimizing a cost function, difference score, or other metric, as afunction of multiple input parameters. Although an iterative process hasbeen described for individual parameter adjustment, multiple,simultaneous parameter adjustments for minimizing a cost function arealso well-known in the art.

FIG. 14 illustrates a medical diagnostic comparison image generatingsystem 1400. System 1400 comprises a computing apparatus 1401, whichcomprises central processing unit(s) (CPU(s)) 1402 and memory 1403,which is a non-transitory computer readable medium. CPU(s) 1402 mayinclude a general purpose processor, a function-specific processor, suchan application specific integrated circuit (ASIC) or a programmed fieldprogrammable gate array (FPGA), or multiple ones of these. Memory 1403,which is coupled to CPU(s) 1402, may comprise volatile memory,non-volatile memory, read only memory (ROM), random access memory (RAM),magnetic memory, optical memory, or another computer readable medium.

Computing apparatus 1401 also comprises a communication module 1404,which provides communication between CPU(s) 1402 and memory 1403, bothwithin computing apparatus 1401, and external systems and devices. Thefunctions of communication module 1404 can be distributed among multipleseparate modules, systems or subsystems, based on the specifics of theinput/output technologies and protocols used by computing apparatus1401. Several external systems are illustrated in system 1400, includingimage collection system 1405, video display 1406, and optical drive1407. Image collection system 1405 may be an x-ray system an MRI system,or another system that can collect medical diagnostic imagery. Videodisplay 1406 is suitable for displaying images to a medicalprofessional, including the baseline images and comparison images.Optical drive 1407 is illustrated as holding optical disk 1408, which isa computer readable optical medium. Optical disk 1408 may contain apatient's prior medical diagnostic images, one or more of which may becompared with a new image collected by image collection system 1405.

Multiple computational modules and data sets are illustrated withinmemory 1403, although it should be understood that computation and datastorage could be distributed among multiple computational nodes. Memory1403 comprises a control module 1409, which provides a GUI for a humanoperator to manually select and adjust images and otherwise control thecomparison image generation process, for example indicating a region ofinterest. Memory 1403 also comprises a processing module 1410.Processing module 1410 may provide some or all of the functionalitydescribed for processing modules 1302 and 1304, pixel comparison andadjustment control module 1305, and anomaly suppression module 1306 ofFIG. 13. Scoring module 1411 and rendering module 1412 are also withinmemory 1403, in the illustrated embodiment. Rendering module 1412 takesin the pixel matrices or adjusted baseline images, and outputs thecomparison image suitable for display on video display 1406. Some of themodules thus described may be located at remote node 1419.

Image database 1413, illustrated as within memory 1403, stores priormedical diagnostic images for the patient, and may read from or write tooptical drive 1407. Images may also be stored in image database 1413 orat remote node 1419. The images should have auxiliary data that includespatient identification and a timestamp, so that a medical professional,with the assistance of scoring module 1411, can identify whether aparticular change is normal or abnormal for a particular lapse in timebetween collecting the baseline images used to generate a comparisonimage. As illustrated, three lower level databases 1414-1416, within thelarger image database 1413, reflect the presence of image sets for threedifferent patients, although a different database hierarchy could beused. A security module 1417 enables a secure, authenticated sessionover internet 1418, to which computing apparatus 1401 is connected, inthe event that any image data is to be retrieved from or sent to aremote node, for example remote node 1419 or another remote node.

Apparatus 1401 is thus configured for generating a medical diagnosticcomparison image, based on multiple baseline medical diagnostic images.A composite comparison image generation module, which is a combinationof at least modules 1409-1412 and 1417, is comparable in function to thecomposition of previously-described comparison and adjustment controlmodule 1305 and anomaly suppression module 1306 in FIG. 13. The requiredfunctions can be distributed and function can be allocated in multipleways. These composite modules are configured to receive a first baselinemedical diagnostic image and a second baseline medical diagnostic imagefrom database 1413; operate on the first baseline medical diagnosticimage and the second baseline medical diagnostic image as matrices ofpixel intensity values; adjust a rotation of at least a portion of oneof the baseline medical diagnostic images relative to the other baselinemedical diagnostic image; adjust a magnification of at least a portionof one of the baseline medical diagnostic images relative to the otherbaseline medical diagnostic image; adjust a horizontal displacement ofat least a portion of one of the baseline medical diagnostic imagesrelative to the other baseline medical diagnostic image; adjust avertical displacement of at least a portion of one of the baselinemedical diagnostic images relative to the other baseline medicaldiagnostic image; adjust an intensity of at least a portion of one ofthe baseline medical diagnostic images relative to the other baselinemedical diagnostic image; create a predominantly intensity-onlycomparison image with a region of pixel intensity difference between thefirst baseline medical diagnostic image and the second baseline medicaldiagnostic image, as processed, having a different hue than apredominant hue of the comparison image; and render the comparison imagefor display on video display 1406.

Apparatus 1401 comprises a scoring module 1411, which is configured tocalculate a score for the comparison image, based on differences betweenthe first baseline medical diagnostic image and the second baselinemedical diagnostic image, as processed. This is similar in function toscoring module 1310 of FIG. 13. A composite comparison image generationmodule, coupled to or including a scoring module, may be furtherconfigured to iteratively adjust rotation, magnification, horizontaldisplacement, vertical displacement, and intensity of at least a portionof one of the baseline medical diagnostic images relative to the otherbaseline medical diagnostic image, in order to minimize a calculatedscore. It should be understood that minimizing a score could comprisefinding a local minimum for the score, rather than finding the globalminimum. This is because some optimization methods known in the art, forexample genetic algorithms, which may be used with the teachings herein,may render a search for a global extremum computationally prohibitive.One optional method that may be used, and which is more likely to find aglobal extremum for a multi-parameter problem, is a sparse sampling ofthe parameter space, followed by a multi-dimensional interpolation, asearch within the interpolated data set for the extremum, and then finesampling in the neighborhood of the identified extremum candidate.

FIG. 15 illustrates a method 1500 of generating a medical diagnosticcomparison image. Method 1500 is a computer-implemented method,implemented in code that is embodied on a computer readable medium andis configured to be executed on a processor. Method 1500 comprises thefollowing processes: receiving a first baseline medical diagnostic imageand a second baseline medical diagnostic image into a computer readablemedium, box 1501; operating on the first baseline medical diagnosticimage and the second baseline medical diagnostic image as matrices ofpixel intensity values, box 1502; adjusting a rotation of at least aportion of one of the baseline medical diagnostic images relative to theother baseline medical diagnostic image, box 1503; adjusting amagnification of at least a portion of one of the baseline medicaldiagnostic images relative to the other baseline medical diagnosticimage, box 1504; adjusting a displacement of at least a portion of oneof the baseline medical diagnostic images relative to the other baselinemedical diagnostic image, box 1505; adjusting an intensity of at least aportion of one of the baseline medical diagnostic images relative to theother baseline medical diagnostic image, box 1506; and creating apredominantly intensity-only comparison image, box 1507. In thecomparison image, a region of pixel intensity difference between thefirst baseline medical diagnostic image and the second baseline medicaldiagnostic image, as processed, has a different hue than a predominanthue of the comparison image.

Method 1500 also comprises rendering the comparison image on a videodisplay, box 1508; calculating a score for the comparison image, basedon differences between the first baseline medical diagnostic image andthe second baseline medical diagnostic image, as processed, box 1509;and iteratively adjusting image parameters to minimize the score, loop1510. During the processing thus described, the matrices (or images, ifthe matrices are retained in an image format during processing) may becropped, expanded and replaced with the values that result values fromdifferent process stages. For example, the process stages of {adjustinga rotation of at least one of the baseline pixel matrices relative tothe other baseline pixel matrix} and {adjusting a magnification of atleast one of the baseline pixel matrices relative to the other baselinepixel matrix} do not necessarily operate o the same set of two or threematrices. A set of two matrices (or images) could be input to theprocess stage of {adjusting a rotation of at least one of the baselinepixel matrices relative to the other baseline pixel matrix}, and theoutput of this stage is a second set of two matrices, perhaps ofdifferent sizes, due to cropping. Then this output set is input to theprocess stage of adjusting a magnification of at least one of thebaseline pixel matrices relative to the other baseline pixel matrix. Itshould be understood that many different programming styles andimplementations can be used that incorporate the inventive aspects ofthe teachings contained herein, and are differently optimized forcomputing efficiency. Therefore the subject matter of the claims is notintended to be limited to a single, unchanging set of matrices processedas described in the teachings herein, and remaining in an unchanginglocation in a commuter memory. Rather the claims should be interpretedto include the substitution of one matrix for another in the variousprocess stages, so long as the substituted matrix contains the relevantinformation derived from the earlier matrix.

If a medical services provider asserts, or allows an agent or legalrepresentative to assert on the provider's behalf, that the teachingscontained herein are obvious as of the priority date of this Applicationfor Patent and acknowledges that the teachings contained herein canimprove the quality of medical care for that provider's patients, butyet had not attempted to avail itself of these teachings as of the datethey allegedly became obvious, then that medical services provider iseffectively admitting to willfully foregoing the use of an obviousimprovement in the quality of medical care. Although Applicant woulddisagree that the teachings herein are obvious, an assertion ofobviousness by medical services provider, without a correspondingattempt to use the allegedly obvious teachings, becomes an admissionthat the medical services provider preferred risking medical malpracticeas an alternative to practicing an obvious improvement in providingmedical care.

Although the invention and its advantages have been described herein, itshould be understood that various changes, substitutions and alterationscan be made without departing from the spirit and scope of the claims.Moreover, the scope of the application is not intended to be limited tothe particular embodiments described in the specification. As one ofordinary skill in the art will readily appreciate from the disclosure,alternatives presently existing or developed later, which performsubstantially the same function or achieve substantially the same resultas the corresponding embodiments described herein, may be utilized.Accordingly, the appended claims are intended to include within theirscope such alternatives and equivalents.

What is claimed is:
 1. A computer implemented method for generating amedical diagnostic comparison image, based on multiple baseline medicaldiagnostic images, the method comprising: receiving a first baselinemedical diagnostic image into a computer readable medium; creating afirst baseline pixel matrix, wherein the first baseline pixel matrixrepresents an intensity of pixels in at least a portion of the firstbaseline medical diagnostic image; receiving a second baseline medicaldiagnostic image into the computer readable medium; creating a secondbaseline pixel matrix, wherein the second baseline pixel matrixrepresents an intensity of pixels in at least a portion of the secondbaseline medical diagnostic image; adjusting a rotation of at least oneof the baseline pixel matrices relative to the other baseline pixelmatrix; adjusting a magnification of at least one of the baseline pixelmatrices relative to the other baseline pixel matrix; adjusting adisplacement of at least one of the baseline pixel matrices relative tothe other baseline pixel matrix; adjusting an intensity of at least aportion of one of the baseline pixel matrices relative to acorresponding portion of the other baseline pixel matrix; creating acomparison image with the first baseline pixel matrix providing a firstcolor information and the second baseline pixel matrix providing asecond color information, such that pixels in the comparison image, thatcorrespond to pixels in the first baseline pixel matrix exceeding anintensity of corresponding pixels in the second baseline pixel matrix,have a hue of the first color, and pixels in the comparison image, thatcorrespond to pixels in the second baseline pixel matrix exceeding anintensity of corresponding pixels in the first baseline pixel matrix,have a hue of the second color.
 2. The method of claim 1 wherein thecomparison image is predominantly a grayscale image.
 3. The method ofclaim 1 wherein adjusting a rotation of a baseline pixel matrixcomprises: generating a new pixel matrix using a method selected fromthe list consisting of: a nearest neighbor method, a linearinterpolation method, and a polynomial interpolation method; andreplacing the baseline pixel matrix with the new pixel matrix.
 4. Themethod of claim 1 wherein adjusting a displacement of a baseline pixelmatrix comprises: generating a new pixel matrix based on a croppedversion of the baseline pixel matrix; and replacing the baseline pixelmatrix with the new pixel matrix.
 5. The method of claim 1 furthercomprising: displaying the comparison image.
 6. The method of claim 5further comprising: displaying at least a portion of the first baselinemedical diagnostic image, in an original or processed state,simultaneously with displaying the comparison image.
 7. The method ofclaim 6 further comprising: displaying at least a portion of the secondbaseline medical diagnostic image, in an original or processed state,simultaneously with displaying at least a portion of the first baselinemedical diagnostic image and the comparison image.
 8. The method ofclaim 1 further comprising: comparing a region of pixel intensitydifference to both an average intensity difference threshold and aminimum dimension threshold; and responsive to the region of pixelintensity difference meeting or exceeding the average intensitydifference threshold and the minimum dimension threshold, causing analert.
 9. The method of claim 1 further comprising: calculating a scorefor the comparison image, using one value selected from the listconsisting of: a dimension of a difference region that meets or exceedsan average intensity difference threshold; a count of difference regionsthat meet or exceed an average intensity difference threshold and aminimum dimension threshold; and a time difference associated with theimages.
 10. The method of claim 1 further comprising: receiving a thirdbaseline medical diagnostic image into the computer readable medium;creating a third baseline pixel matrix, wherein the third baseline pixelmatrix represents an intensity of pixels in at least a portion of thethird baseline medical diagnostic image; adjusting a rotation of thethird baseline pixel matrices relative to the other baseline pixelmatrices; adjusting a magnification of the third baseline pixel matricesrelative to the other baseline pixel matrices; adjusting a displacementof the third baseline pixel matrices relative to the other baselinepixel matrices; adjusting an intensity of at least a portion of thethird baseline pixel matrices relative to corresponding portions of theother baseline pixel matrices; wherein creating a comparison imagecomprises creating a predominantly intensity-only image with the thirdbaseline pixel matrix providing a third color information.
 11. Themethod of claim 1 further comprising: receiving from a graphical userinterface (GUI) an indication of a region of interest, wherein creatinga comparison image comprises creating an image that illustrates theregion of interest.
 12. A computer program embodied on a computerexecutable medium and configured to be executed by a processor, theprogram comprising: code for receiving a first baseline medicaldiagnostic image and a second baseline medical diagnostic image into acomputer readable medium; code for operating on the first baselinemedical diagnostic image and the second baseline medical diagnosticimage as matrices of pixel intensity values; code for adjusting arotation of at least a portion of one of the baseline medical diagnosticimages relative to the other baseline medical diagnostic image; code foradjusting a magnification of at least a portion of one of the baselinemedical diagnostic images relative to the other baseline medicaldiagnostic image; code for adjusting a displacement of at least aportion of one of the baseline medical diagnostic images relative to theother baseline medical diagnostic image; code for adjusting an intensityof at least a portion of one of the baseline medical diagnostic imagesrelative to the other baseline medical diagnostic image; code forcreating a predominantly intensity-only comparison image with a regionof pixel intensity difference between the first baseline medicaldiagnostic image and the second baseline medical diagnostic image, asprocessed, having a different hue than a predominant hue of thecomparison image; and code for rendering the comparison image on a videodisplay.
 13. The computer program of claim 12 wherein the comparisonimage is predominantly a grayscale image.
 14. The computer program ofclaim 12 further comprising: code for calculating a score for thecomparison image, based on differences between the first baselinemedical diagnostic image and the second baseline medical diagnosticimage, as processed.
 15. The computer program of claim 14 furthercomprising: code for iteratively adjusting rotation, magnification,horizontal displacement, vertical displacement, and intensity of atleast a portion of one of the baseline medical diagnostic imagesrelative to the other baseline medical diagnostic image, in order tominimize the score.
 16. An apparatus for generating a medical diagnosticcomparison image, based on multiple baseline medical diagnostic images,the apparatus comprising: a processor; a computer readable mediumcoupled to the processor, the computer readable medium comprising: adatabase of medical diagnostic images; and a comparison image generationmodule configured to: receive a first baseline medical diagnostic imageand a second baseline medical diagnostic image from the database;operate on the first baseline medical diagnostic image and the secondbaseline medical diagnostic image as matrices of pixel intensity values;adjust a rotation of at least a portion of one of the baseline medicaldiagnostic images relative to the other baseline medical diagnosticimage; adjust a magnification of at least a portion of one of thebaseline medical diagnostic images relative to the other baselinemedical diagnostic image; adjust a displacement of at least a portion ofone of the baseline medical diagnostic images relative to the otherbaseline medical diagnostic image; adjust an intensity of at least aportion of one of the baseline medical diagnostic images relative to theother baseline medical diagnostic image; create a predominantlyintensity-only comparison image with a region of pixel intensitydifference between the first baseline medical diagnostic image and thesecond baseline medical diagnostic image, as processed, having adifferent hue than a predominant hue of the comparison image; and renderthe comparison image for display on a video display.
 17. The apparatusof claim 16 further comprising: a scoring module embodied on a computerreadable medium coupled to the processor, the scoring module configuredto calculate a score for the comparison image, based on differencesbetween the first baseline medical diagnostic image and the secondbaseline medical diagnostic image, as processed.
 18. The apparatus ofclaim 17 wherein the comparison image generation module is furtherconfigured to iteratively adjust rotation, magnification, horizontaldisplacement, vertical displacement, and intensity of at least a portionof one of the baseline medical diagnostic images relative to the otherbaseline medical diagnostic image, in order to minimize the score.